How to create beautiful plots using Seaborn in python

If you are tired of basic looking charts and want to create stunning, publication ready visualizations with just a few line of code then Seaborn is your best friend.

In this tutorial , i will walk you through the basics of Seaborn and show you how to make elegant visualizations quickly and easily.

What are we building?

We are going to create :

• A colorful bar chart
• A smooth line plot
• A simple scatter plot
• And a clean box plot

All using Seaborn – a high level plotting library built on top of matplotlib.

How does it work?

Seaborn simplifies visualizations by:

• Automatically applying beautiful styles
• Handling grouped data easily
• Integrating perfectly with pandas Data Frames

Just give Seaborn your Data Frames and column names and it does the rest.

1. Bar Plot

A bar plot shows the average value of a  variable for each category. If you have grouped or categorial data and want to compare group means , this is your go to.

Use case:

• Track revenue or sales growth over time
• Compare trends across multiple groups(e.g.  departments, regions)

Code:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'HR', 'HR', 'Engineering', 'Engineering'],
    'Month': ['Jan', 'Feb', 'Jan', 'Feb', 'Jan', 'Feb'],
    'Revenue': [100000, 120000, 80000, 90000, 150000, 160000]
})
sns.set_theme(style="whitegrid")
sns.barplot(data=data, x='Month', y='Revenue', hue='Department')
plt.title("Bar Plot: Monthly Revenue by Department")
plt.show()

Output:

2. Line Plot

A line plot is used for time series or ordered data. It connects data points with lines and is ideal for observing trends over time.

Use Case:

• Track revenue or sales growth over time
• Compare trends across multiple groups

Code:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'HR', 'HR', 'Engineering', 'Engineering'],
    'Month': ['Jan', 'Feb', 'Jan', 'Feb', 'Jan', 'Feb'],
    'Revenue': [100000, 120000, 80000, 90000, 150000, 160000]
})
sns.set_theme(style="darkgrid")
sns.lineplot(data=data, x='Month', y='Revenue', hue='Department', marker='o')
plt.title("Line Plot: Monthly Revenue Trend")
plt.show()

Output:

3. Scatter Plot

A scatter Plot shows the relationship between two continuous variables. You can group by category using color and shape.

Use Case:

• Visualize correlation(e.g. revenue vs customer count)
• Spot clusters, trends, outliers

Code:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'HR', 'HR', 'Engineering', 'Engineering'],
    'Month': ['Jan', 'Feb', 'Jan', 'Feb', 'Jan', 'Feb'],
    'Revenue': [100000, 120000, 80000, 90000, 150000, 160000]
})
sns.set_theme(style="white")
sns.lineplot(data=data, x='Month', y='Revenue', hue='Department', style='Department')
plt.title("Scatter Plot: Revenue by Month and Department")
plt.show()

Output:

4. Box Plot

A box plot shows the distribution of data through

• Median(Center line)
• Quartiles(box edges)
• Min/Max values(whiskers)
• Outliers(dots)

Use Case:

• Analyze spread and skewness of revenue across departments
• Detect outliers and variance

Code:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.DataFrame({
    'Department': ['Sales', 'Sales', 'HR', 'HR', 'Engineering', 'Engineering'],
    'Month': ['Jan', 'Feb', 'Jan', 'Feb', 'Jan', 'Feb'],
    'Revenue': [100000, 120000, 80000, 90000, 150000, 160000]
})
sns.set_theme(style="ticks")
sns.boxplot(data=data, x='Department', y='Revenue')
plt.title("Box Plot: Revenue Distribution by Department")
plt.show()

Output: